In today’s digital-driven world, human conversation has become a somewhat neglected element of the online experience.
In 2021, an estimated 85% of online visitor interaction is handled without human intervention, making chatbot systems an integral part of the sales and marketing process.
As humans, we value real human interaction above all else in the customer support process, prospects desire conversational engagement that feels authentic, vivid and memorable – and this goes a long way.
AI technology advancements have caught onto this trend, developing chatbot systems that offer a more ‘real life’ experience: conversational AI.
But what is conversational AI? And how does it differ from traditional chatbot technology?
This latest article will offer a 101 guide to everything you need to know about chatbots and conversational AI systems, breaking down why so many businesses are starting to adopt them and how you can implement them into your digital sales strategy moving forward.
What are Chatbots?
Chatbots have fast-become one of the most popular brand communication channels the world over, increasing a whopping 92% between 2019 and 2020 – and for good reason!
According to Comm100, chatbots handle nearly 70% of customer-led conversations from start to finish, heavily reducing the need for human-based customer service interaction and freeing up the reliance on physical staff significantly.
Before we delve any deeper, let’s take a look at what a chatbot actually is, and the advantageous role it plays in the B2B market across the globe.
At its most basic level, a chatbot is a programmed system that gives website visitors the opportunity to interact with a brand using a digital device, as if they were communicating with a human being.
Typically, chatbots tend to adopt a text-based approach, through messaging pop-ups or a website-integrated chat interface – allowing the user to hold semi-intelligent conversations and granting streamlined access to common query solutions.
Generally speaking, when we talk about traditional chatbots we’re referring to a rules-based bot – systems programmed with a predefined workflow built around pre-written questions and automated answers.
In layman’s terms, rules-based chatbot conversations exist long before an inquiry is made. The visitor triggers a conversation through a pre-written question and the chatbot guides them through a mapped-out, FAQ-style conversation flow-chart – with no ability to deviate.
Hang on, we know what you’re thinking: Why are chatbots SO popular when authentic human interaction is so highly valued?
Why should I use a Chatbot?
The answer to this question is simple: Rules-based chatbot measures reduce the strain on providers and consumers.
The traditional customer service model is noisy, chaotic, and highly impractical for the user. Think slow response email systems and the dreaded customer service phone queue.
The average time spent on hold is 90 seconds, with an estimated 60% of people hanging up within the first minute. Understandably, a consistent problem for businesses.
The purpose of a rules-based chatbot system is two-fold: they provide an instantaneous resource for simple FAQ-driven questions while also mitigating the reliance on human-led intervention.
Think of it this way: no two people are the same, and humans introduce a great deal of variance to the question-response process; they don’t follow the same conversation over and over again the same way.
Rules-based chatbots mitigate this variance, streamlining visitor inquiries through a pre-designed problem-answer-response dialogue funnel, and with several advantages:
How does Conversational AI work?
Combining natural language processing (NLP) and machine learning (ML), conversational AI develops a constant feedback flow that consistently improves the AI’s conversational understanding.
Developed by advanced algorithms, ML allows the chatbot to increase its capabilities with every input. In short, as the human variance is input, the conversational AI chatbot develops learnings, and the algorithm improves, allowing it to constantly process, understand and converse in a modernized and human way.
But, while machine learning can be beneficial, it isn’t alone in the conversational AI process. When partnered with NLP, the chatbot elevates to a whole new level of human conversational functionality.
Through sophisticated, ingrained natural learning technology, NLP furthers the bots’ understanding of language, enabling greater conversational possibilities and furthering the AI to unmatched automated parallels, simulating that human experience with verifiable finite detail.
Key Differences & Considerations
There are several primary differences in the chatbot versus conversational AI debate. In its most primitive form, it’s worth establishing that chatbots and conversational AIs aren’t opponents – they work hand in hand to elevate user experience.
Conversational AI is an advancement that not only softens the landing where the traditional rules-based chatbot falls short but takes the system to an entirely new level of interactivity.
As we know, a rules-based chatbot is a bounded system with predefined, limited categories.
Conversational AI, by comparison, is focused on dialogue systems that allow the chatbot to overcome human variance, offering a better understanding of visitor intent and compensating when the user has an unexpected problem or changes the subjectivity of their request.
We’re no longer constrained by words going back and forth, conversational AI truly opens the door to all kinds of messaging, engagement and actionable possibilities, where Rules-based chatbot systems just don’t come close.
Think of it like this: conversational AI can be used to enhance chatbots, making them more intelligible and effective. But not all chatbots are powered by conversational AI. Conversational AI physically resolves issues, whereas the traditional chatbot offers an information resource on rigid, common queries.